Foundations of Generative AI Models and Development Platforms

Generative artificial intelligence is transforming how we build software, analyze data, and solve complex problems. To harness this power, you need to understand the underlying models and the platforms that host them. This written course provides a clear, conceptual path through the landscape of generative AI. You will move from foundational definitions of deep learning to exploring how massive pre-trained models are built, deployed, and integrated into modern applications. What you'll learn: - Understand the core mechanics of deep learning, neural networks, and transformer architectures. - Explore the capabilities of pre-trained foundation models and large language models (LLMs). - Compare leading AI development platforms and deployment environments to choose the right tools for your projects. - Apply fundamental prompt engineering techniques to guide model outputs effectively. - Learn the basics of Retrieval-Augmented Generation (RAG) and how vector databases enhance AI accuracy. - Identify key ethical considerations, safety guardrails, and bias mitigation strategies in AI development. The course begins with essential terminology and the evolution of neural networks before guiding you through model selection, platform integration, and modern implementation patterns. Through clear written explanations and structured text-based exercises, you will build a solid working knowledge of generative AI. This course is designed for beginners, developers, and tech enthusiasts looking for a clear entry point into generative AI, requiring no prior machine learning experience. Start reading today to build your foundation in generative AI and prepare for the future of technology.